In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the complex scattering of electromagnetic waves on the rough sea surface, the neural network approach is adopted to develop an algorithm to derive significant wave height (SWH) from CYGNSS data. Eighty-nine million pieces of CYGNSS data from September to November 2020 and the co-located ECMWF data are employed to train a three-hidden-layer neural network. Ten variables are considered as the input parameters of the neural network. Without the auxiliary of the wind speed, the SWH retrieved using the trained neural network exhibits a bias and an RMSE of −0.13 and 0.59 m with respect to ECMWF data. When considering wind speed as the input, the bias and RMSE were reduced to −0.09 and 0.49 m, respectively. When the incidence angle ranges from ${35}^{°}$ to ${65}^{°}$ and the SNR is above 7 dB, the retrieval performance is better than that obtained using other values. The measurements derived from the “Block III” satellite offer worse results than those derived from other satellites. When the distance is considered as an input parameter, the retrieval performances for the areas near the coast are significantly improved. A soft data filter is used to synchronously improve the precision and ensure the desired sample number. The RMSEs of the retrieved SWH are reduced to 0.45 m and 0.41 m from 0.59 m and 0.49 m, and only 16.0% and 14.9% of the samples are removed. The retrieved SWH also shows a clear agreement with the co-located buoy and Jason-3 altimeter data.

Global Navigation Satellite System Reflectometry Bistatic Synthetic Aperture Radar (GNSS-R BSAR) is becoming more and more important in remote sensing because of its low power, low mass, low cost, and real-time global coverage capability. The Back Projection Algorithm (BPA) was usually selected as the GNSS-R BSAR imaging algorithm because it can process echo signals of complex geometric configurations. However, the huge computational cost is a challenge for its application in GNSS-R BSAR. Graphics Processing Units (GPU) provides an efficient computing platform for GNSS-R BSAR processing. In this paper, a solution accelerating the BPA of GNSS-R BSAR using GPU is proposed to improve imaging efficiency, and a matching pre-processing program was proposed to synchronize direct and echo signals to improve imaging quality. To process hundreds of gigabytes of data collected by a long-time synthetic aperture in fixed station mode, a stream processing structure was used to process such a large amount of data to solve the problem of limited GPU memory. In the improvement of the imaging efficiency, the imaging task is divided into pre-processing and BPA, which are performed in the Central Processing Unit (CPU) and GPU, respectively, and a pixel-oriented parallel processing method in back projection is adopted to avoid memory access conflicts caused by excessive data volume. The improved BPA with the long synthetic aperture time is verified through the simulation of and experimenting on the GPS-L5 signal. The results show that the proposed accelerating solution is capable of taking approximately 128.04 s, which is 156 times lower than pure CPU framework for producing a size of 600 m × 600 m image with 1800 s synthetic aperture time; in addition, the same imaging quality with the existing processing solution can be retained.

Based on a developed three-layerair–ice–water reflection model, this article simulates the evolution of reflection coefficient versus elevation angle. Due to the interference between the signal components from the air–ice and ice–water interfaces, the reflection coefficient experiences an oscillating pattern versus elevation angle so that detecting sea ice using the power or amplitude of the reflected global navigation satellite system (GNSS) signal has to choose a suitable satellite to reduce the influence of the oscillating pattern. A sea ice surface is more stable and presents higher correlation than a dynamic ocean surface, this article explores the potential of detecting and measuring sea ice using the coherency of reflected GNSS signal for coastal scenario. Experimental results show that phase coherency can significantly detect sea ice without strictly limiting elevation and azimuth angle. This article also is to is to explore the potential of retrieving sea ice thickness using the oscillating phase pattern versus elevation angle. The phase compensation and the dual-polarization observation are proposed to remove the delay phase between the direct and reflected signal from the estimated phase of the reflected GNSS signal. The results show that the amplitude and frequency of the oscillating phase pattern, respectively, have an inversely proportional and positively linear relationship with sea ice thickness. Simulation shows that, compared to the oscillating amplitude, the oscillating frequency is a better choice to measure sea ice thickness. The frequency of the dual-polarization oscillating pattern could provide the measurement performance with a root-mean-square error of 0.05 m.

The diversity and complexity of the environment, such as wind speed, wave height, sea clutter, etc., are the main reasons for the current limitation in the spaceborne Global Navigation Satellite System-Reflectometry(GNSS-R) sea surface height(SSH) accuracy. This letter proposes a modified ResNet architecture for sea surface altimetry, named fractional Fourier transform-ResNet (F-ResNet), using filtered Delay-Doppler Maps(DDMs) and other information from Cyclone Global Navigation Satellite System(CYGNSS) Level 1 data. The DDMs are filtered by fractional Fourier transform(FrFT) to keep the doppler and delay information in the five maps according to the different fractions. F-ResNet is based on convolutional layers for the filtered DDMs, along with fully connected layers for processing ancillary technical and higher-level input parameters. The validity of our proposed method is demonstrated using the Jason-3 data set for the whole year 2020. The RMSE of the SSH using F-ResNet is improved by 15.2% compared to other machine learning methods, which can achieve 1.17 meters. And the influence of different signal-to-noise ratios on prediction accuracy is analyzed.

A study investigating the feasibility of retrieving wind direction using low-altitude backward geometry of Global Navigation Satellite System-Reflectometry (GNSS-R) is implemented in this paper. This paper first focuses on analyzing the influence of wind direction on the roughness of sea surface and scattering strength. The results show that backward scattered GNSS signals obviously change over wind direction despite of the weaker power level than the forward signals. Then, the power link of backward scattered GNSS signal and the visible number of the satellites composing the bistatic backward observation geometry with the receiver are analyzed. Backward scattered GNSS signals are lower about $10\text{--}20$ dB than forward ones; hence, a higher gain left-hand circularly polarization antenna should be used to receive them. In the same antenna view, theoretically the backward scattered signals from about three satellites could be received. Finally, a retrieving algorithm of wind direction based on matching theory is proposed. The retrieving results using simulated data show that, first, multibeam observation (at least three-beam observation) should be utilized to remove the uncertainty of retrieving wind direction, second, the accuracy of wind speed and the signal-to-noise ratio (SNR) of the delay waveform importantly impact on the retrieving performance of wind direction, therefore, it is needed to improve the measuring accuracy of wind speed and the SNR. In one word, all results mentioned above give the guide information to observe wind direction using low-altitude GNSS-R.